Industrial manufacturers are facing a convergence of pressures that legacy systems were never built to handle, including supply chain instability, labor shortages, margin pressure, and rising expectations for faster decision-making.
At the same time, AI is shifting from experimentation to operational necessity, enabling manufacturers to turn data into action and build more adaptive, resilient operations. The challenge is no longer digital transformation alone, but scaling AI to drive execution at the speed disruption demands.
Recently, IEN's Executive Editor Anna Wells and QAD's Stephen Dombroski explored what AI modernization means in practice and where it is already delivering measurable impact across manufacturing operations.
Industrial manufacturers are facing a convergence of pressures that legacy systems were never built to handle, including supply chain instability, labor shortages, margin pressure, and rising expectations for faster decision-making.
At the same time, AI is shifting from experimentation to operational necessity, enabling manufacturers to turn data into action and build more adaptive, resilient operations. The challenge is no longer digital transformation alone, but scaling AI to drive execution at the speed disruption demands.
Recently, IEN's Executive Editor Anna Wells and QAD's Stephen Dombroski explored what AI modernization means in practice and where it is already delivering measurable impact across manufacturing operations.
What you will take away from viewing:
- Why most AI initiatives stall and how to move from pilots to execution
- Where AI and agentic AI are delivering real operational value today
- How ERP is evolving into a real-time system of action
- A practical framework for AI modernization in industrial manufacturing
See poll results from our live audience and a lively discussion and question/answer session.
